Mangroves play pivotal roles in ecosystem services, but anthropogenic pressures contribute to their alarming degradation. Precise quantification of vital vegetation characteristics, particularly leaf area index (LAI), is crucial for effective monitoring. LAI serves as a key biophysical parameter in assessing vegetation structure, eco-physiological processes, and overall health. This study pioneers the exploration of a hybrid model (combining radiative transfer with machine learning) for LAI estimation in mangroves. Employing (INvertible FOrest Reflectance Model) INFORM and support vector regression in Bhitarkanika Wildlife Sanctuary, India, we utilized digital hemispherical photographs and Sentinel-2 optical data. Results reveal INFORM's superior retrieval capacity (RMSE = 2.56 m2.m−2) over the PROSAIL model (RMSE = 0.73 m2.m−2). The study underscores the efficacy of physical-based models, particularly INFORM, in accurate LAI estimation, particularly in challenging environments like mangrove ecosystems where in-situ data collection is constrained.

The Global Ecosystem Dynamics Investigation (GEDI) aims to provide improved characterization of forest structure, and plant area index (PAI) is one of many variables provided in the official GEDI Level 2B (L2B) product suite. However, since release, few quantitative validation studies have been conducted. To reach Stage 1 of the validation hierarchy proposed by the Land Product Validation (LPV) sub-group of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV), we provide an initial assessment of PAI estimates from GEDI’s L2B product. This is achieved using 18 in situ reference measurements available through the Copernicus Ground Based Observations for Validation (GBOV) service. We show that GEDI L2B PAI retrievals provide a nearly unbiased estimate of effective (PAIe) (RMSD = 0.95, bias = 0.02, slope = 1.07), but systematically underestimate PAI (RMSD = 1.42, bias = -0.91, slope = 0.77). This is attributed to an assumed random distribution of plant material in the algorithm. To reach Stage 2 of the CEOS WGCV LPV hierarchy, continued work is needed to validate the product against additional in situ reference measurements covering further locations and time periods.

Unmanned aerial vehicles (UAVs) equipped with different sensors can provide data with high spatiotemporal resolution and have broad application prospects. During the flight of the UAV, changes in illumination, exposure time, etc., will cause different degrees of radiometric differences between images, resulting in a calibration relationship established on a single image that cannot be applied to other images; in addition, the vignetting effect also significantly changes the brightness distribution inside an image, thus posing challenges for radiometric calibration of UAV images. In this paper, based on block adjustment (BA), we proposed a radiometric block adjustment model under the consideration of vignetting and the light-dark differences between images. The proposed method requires only a small number of calibration blankets, thus reducing the complexity of the experiment. The results from two study areas showed that the proposed method could compensate for vignetting to a certain extent and the radiometric consistency of the two datasets was improved from 12.9%~21.8% to 4.7%~12.7%. Validated using ground samples, the mean RMSE and MRPE of all five bands were 0.054, 21.8%, and 0.037, 20.4% in the two study areas, respectively. The total uncertainty was less than 8.1%. When there were obvious light-dark differences between images, such as in the visible light bands, our method could significantly improve the accuracy of the radiometric calibration.

This presentation will focus on GBOV activities, introducing the service, its distribution system, and its growing network as well as its achievements after the first operation phase. Some examples from the GBOV ground stations will be reported to illustrate the instrument setup and datasets and more importantly the lessons learnt from the first phase. GBOV phase 1 has been a success thanks to a large community of scientist providing high quality dataset. GBOV phase 2 is willing to strengthen its relationship with ground network and go further in stimulating interactions with the global community.

To better characterize the temporal dynamics of vegetation biophysical variables, a variety of automated in situ measurement techniques have been developed in recent years. In this study, we investigated automated digital hemispherical photography (DHP) and wireless quantum sensors, which were installed at two sites under the Copernicus Ground Based Observations for Validation (GBOV) project. Daily estimates of plant area index (PAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) were obtained, which realistically described expected vegetation dynamics. Good correspondence with manual DHP and LAI-2000 data (RMSE = 0.39 to 0.90 for PAI, RMSE = 0.07 for FAPAR) provided confidence that the investigated approaches can deliver data of comparable quality to traditional in situ measurement techniques.

The Copernicus Ground-Based Observations for Validation (GBOV) service aims to develop and distribute robust in-situ datasets from a selection of ground-based monitoring sites for a systematic and quantitative validation of Earth Observation (EO) land products. The EO land products of particular interest are those from the Copernicus Global Land Service (CGLS), but GBOV data usage is not restricted to CGLS products and is fully open to the entire community, following the general Copernicus data policy. In this paper, a global overview of GBOV service is shown, and the attention is focused on the vegetation type products: Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Available Radiation (FAPAR) and the Fraction of Covered ground (FCover). An introduction of the algorithm implemented to compute these products, mainly from upscaling of ground-based observation with high resolution satellite images, is presented.

Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Sentinel-3 Ocean and Land Colour Instrument (OLCI), and to ensure its utility in a wide range of operational applications, validation efforts are required. In the past, these activities have been constrained by the need for costly airborne hyperspectral data acquisition, but the Sentinel-2 Multispectral Instrument (MSI) now offers a promising alternative. In this paper, we explore the synergetic use of Sentinel-2 MSI data for validation of the Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI) over the Valencia Anchor Station, a large agricultural site in the Valencian Community, Spain. High retrieval accuracy (RMSE = 0.20 g m-2) was obtained by applying machine learning techniques to Sentinel-2 MSI data, highlighting the valuable information it can provide when used in synergy with Sentinel-3 OLCI data for land product validation.

This research describes the seasonal phenological pattern of Africa's vegetation and its recent trends using MODIS EVI time-series data with a relatively fine spatial resolution of 500 m and a long temporal range of 15 years (2001-2015). The objectives were to measure the vegetation phenology of the major land cover types and determine the temporal trends across the geographical sub-regions of Africa. An improved representation of the land surface phenology (LSP) of Africa is provided, revealing which land cover types and regions have undergone significant changes in phenology over the period 2001-2015. Recommendations are given for future studies needed to determine and distinguish all the drivers of vegetation phenology.

Sentinel-2 is a wide-swath and fine spatial resolution satellite imaging mission designed for data continuity and enhancement of the Landsat and other missions. The Sentinel-2 data are freely available at the global scale, and have similar wavelengths and the same geographic coordinate system as the Landsat data, which provides an excellent opportunity to fuse these two types of satellite sensor data together. In this paper, a new approach is presented for the fusion of Landsat 8 Operational Land Imager and Sentinel-2 Multispectral Imager data to coordinate their spatial resolutions for continuous global monitoring. The 30 m spatial resolution Landsat 8 bands are downscaled to 10 m using available 10 m Sentinel-2 bands. To account for the land-cover/land-use (LCLU) changes that may have occurred between the Landsat 8 and Sentinel-2 images, the Landsat 8 panchromatic (PAN) band was also incorporated in the fusion process. The experimental results showed that the proposed approach is effective for fusing Landsat 8 with Sentinel-2 data, and the use of the PAN band can decrease the errors introduced by LCLU changes. By fusion of Landsat 8 and Sentinel-2 data, more frequent observations can be produced for continuous monitoring (this is particularly valuable for areas that can be covered easily by clouds, thereby, contaminating some Landsat or Sentinel-2 observations), and the observations are at a consistent fine spatial resolution of 10 m. The products have great potential for timely monitoring of rapid changes.

Envisat's Medium Resolution Imaging Spectrometer (MERIS) acquired multi-spectral imagery of the Earth in the optical domain over terrestrial surfaces for a decade at global scale. For the last ten years, scientists have used multi-spectral data or terrestrial geophysical products for characterizing the state of the global system and its variability. Our paper shows highlights of several achievements of the use of MERIS data over terrestrial surfaces but specifically focuses on regional to global scale applications. We first summarize daily operational biophysical parameters and present examples of their uses for the monitoring of land surface states and changes, especially related to ECVs. In addition, specific projects for deriving a series of land cover maps will be presented and we conclude on the MERIS data exploitation and highlights future applications.

This letter evaluates three Envisat Medium Resolution Imaging Spectrometer Terrestrial Chlorophyll Index (MTCI)-based models for the estimation of terrestrial gross primary productivity (GPP) across a range of vegetation types. Correlations between flux tower measures of GPP and models for years between 2003 and 2007 were established for 30 sites across USA, Canada, and Brazil. Correlations were seen to range from very strong to weak, depending on seasonal variation in photosynthetic capacity (which is influenced by chlorophyll content) exhibited by the vegetation at each site. At least one of the three models obtained a statistically significant relationship with GPP at every site. Results indicate that chlorophyll content (as measured by the MTCI) is a most relevant community property for estimating primary productivity and chlorophyll-related vegetation indexes provide favorable approximations of the GPP of terrestrial vegetation. The inclusion of radiation information (photosynthetically active radiation (PAR) and fraction of photosynthetically active radiation (fPAR)) into the models extended the applicability of the models and the accuracy of the GPP estimate. Although further investigation is required to fully understand the applicability of these models and their parameters, these results point to the possibility of a total remote sensing approach to GPP estimation.

This paper explores the potential of using the Medium Resolution Medium Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) for estimating gross primary productivity across a range of vegetation cover types. Correlations between flux tower measures of GPP and corresponding MTCI for years between 2003 and 2007 were established for 30 sites across the Americas. Correlations were seen to range from very strong to weak depending on variation in chlorophyll content exhibited. Moreover, comparison of the performance of the MTCI against the MOD17 GPP product at selected sites indicated that overall, the correlation between flux tower and MOD17 GPP estimates were not as strong as obtained when using the MTCI. These results demonstrate the potential of this approach and should be developed further.

Phenological information can be provided globally using remote sensing based time-series vegetation indices. Basic differences in the data and methods used can yield different results. This study analysed such differences in the phenological information, mainly onset of greenness (OG), estimated using the Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data and the Terrestrial Chlorophyll Index (MTCI) from Medium Resolution Imaging Spectrometer (MERIS) data. The two datasets were processed independently using different techniques to provide weekly estimates. Differences in the OG results were analysed for two years (2003 & 2006) and at four levels: a) full study area, b) within land cover classes, c) within core zones of each class and d) at the edge zones of each class. It was found that the trend of OG estimated from MODIS and MERIS were spatially similar, although not the same. From 15 Biome classes found in the study area the classes with the greatest differences were evergreen needle leaf, mixed forest and cropland. The differences were mainly due to the characteristic nature of the indices and also, to some extent, due to false internal flags in the algorithms.

Large concentrations of herbicide were sprayed onto the forests of southern Vietnam in the 1960s and early 1970s. Over 30 years later, many of these contaminated forests have regained full canopy cover, albeit with reduced chlorophyll content. The European Space Agency produces an operational product for the estimation of terrestrial chlorophyll content over large areas of terrain. This product uses data recorded by the Medium Resolution Imaging Spectrometer (MERIS) on Envisat and is called the MERIS Terrestrial Chlorophyll Index (MERIS). The relationship between historical levels of herbicide contamination and contemporary MTCI was strong (R = 0.86) and negative, with high levels of herbicide contamination being associated (via low levels of chlorophyll concentration) with low levels of MTCI. This is the first published study to demonstrate a relationship between MTCI and a surrogate for chlorophyll content. The next stage of this research is to build on the strength of this relationship and use contemporary MTCI to estimate historical herbicide levels during the 1960s and 1970s across southern Vietnam.

The medium resolution imaging spectrometer (MERIS), one of the payloads on Envisat, has fine spectral resolution, moderate spatial resolution and a three day repeat cycle. This makes MERIS a potentially valuable sensor for the measurement and monitoring of terrestrial environments at regional to global scales. The red edge, which results from an abrupt change in reflectance in red and near-infrared wavelength has a location that is related directly to the chlorophyll content of vegetation. A new index called the MERIS terrestrial chlorophyll index (MTCI) uses data in three red/NIR wavebands centred at 681.25 nm, 705 nm and 753.75 nm (bands 8, 9 and 10 in the MERIS standard band setting). The MTCI is easy to calculate and can be automated. Preliminary indirect evaluation using model, field and MERIS data suggested its sensitivity, to notably high values of chlorophyll content and its limited sensitivity to spatial resolution or atmospheric effects. As a result this index is now a standard level-2 product of the European Space Agency. For direct MTCI evaluation two different approaches were used. First, the MTCI/chlorophyll content relationship were determined using a chlorophyll content surrogate for sites in southern Vietnam and second, the MTCI/chlorophyll relationship was determined using actual chlorophyll content for sites in the New Forest, UK and for plots in the greenhouse. Forests in southern Vietnam were contaminated heavily with Agent Orange during the Vietnam War. The contamination level was so high that it led to a long term decrease in chlorophyll content within forests that have long since regained full canopy cover. In this approach the amount of Agent Orange dropped on to the forest between 1965 and 1971 was used as a surrogate (inverse) for contemporary chlorophyll content and was related to current MTCI at selected forest sites. The resulting relationship was negative. Further per pixel investigation of the MTCI/Agent Orange concentration relationship is under way for large forest regions. In the second approach MTCI was related directly to chlorophyll content at two scales and the initial resulting relationships were positive. Further plans involve the evaluation of the MTCI at local, regional and eventually global scales.